Compiler optimizations for real time execution of loops on limited memory embedded systems

We propose a framework to carry out an efficient data partitioning for global arrays on limited on-chip memory embedded systems. The key problem addressed in this work is how to perform a good partitioning of data references encountered in loops between on-chip and off-chip memory to meet the demands of real time response by keeping run time overheads of remote access to a minimum. We introduce a concept of footprint to precisely calculate the memory demands of references at compile time and compute a profit value of a reference using its access frequency and reuse factor. We then develop a methodology based on 0/1 knapsack algorithm to partition the references in the local/remote memory. We show the performance improvements due to our approach and compare the results.